Are individual analyses of multiple short urine collections throughout the 24 hours superior to a standard 24‐hour urine collection in precipitation risk assessment of healthy subjects?
Bibliographic record
Abstract
PURPOSE: The commonly used 24-hour collection technique has been the mainstay of diagnosis for supersaturation but has some certain limitations. Hence, superiority of multiple short urine collections as a new alternative in precipitation risk assessment was assessed compared to the standard 24-hour urine collection among healthy subjects. MATERIALS AND METHODS: Individual urine samples of 26 healthy subjects were acquired every 2 to 3 hours throughout the 24 hours. Urine samples were obtained and the time and volume of each sample were recorded. Urinary constituents involved in precipitation including, sodium-potassium, chloride, calcium, phosphate, citrate, magnesium, urea, creatinine and pH were measured. A simulated 24-hour collection was recalculated by the totalling of all shorter urine collections volume and urinary constituents excretions throughout the day. RESULTS: Urine pH, urine creatinine and precipitation rate had a significantly lower values in 24-hours urine collection compared to one individual value of multiple urine collections by -0.769 (P < .0001), -7.305 (P < .0001), and - 12.838 (P < .0001), respectively. However, calcium (2.697, P < .0001), citrate (3.54, P < .0001), total phosphate (19.961, P < .0001) and total creatinine (9.579, P < .0001) had statistically significantly higher values in the 24-hours urine collection compared to individual value of multiple urine collections. CONCLUSION: Based on the results, individual analysis of multiple shorter urine collections throughout the day improves the ability of identifying supersaturation points, precipitation risk zones and may potentially improve risk assessment compared to the 24-hour urine collection method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".